data structures and algorithmsgalles/cs245/lecture/lecture6.pdf · cs245-2017s-06 binary search...

Data Structures and AlgorithmsCS245-2017S-06

Binary Search Trees

David Galles

Department of Computer Science

University of San Francisco

06-0: Ordered List ADT

Operations:

Insert an element in the list

Check if an element is in the list

Remove an element from the list

Print out the contents of the list, in order

06-1: Implementing Ordered List

Using an Ordered Array – Running times:

Check

Insert

Remove

Print

06-2: Implementing Ordered List

Using an Ordered Array – Running times:

Check Θ(lg n)

Insert Θ(n)

Remove Θ(n)

Print Θ(n)

06-3: Implementing Ordered List

Using an Unordered Array – Running times:

Check

Insert

Remove

Print

06-4: Implementing Ordered List

Using an Unordered Array – Running times:

Check Θ(n)

Insert Θ(1)

Remove Θ(n) Need to find element first!

Print Θ(n lg n)

(Given a fast sorting algorithm)

06-5: Implementing Ordered List

Using an Ordered Linked List – Running times:

Check

Insert

Remove

Print

06-6: Implementing Ordered List

Using an Ordered Linked List – Running times:

Check Θ(n)

Insert Θ(n)

Remove Θ(n)

Print Θ(n)

06-7: The Best of Both Worlds

Linked Lists – Insert fast / Find slow

Arrays – Find fast / Insert slow

The only way to examine nth element in a linkedlist is to traverse (n-1) other elements

4 8 12 15 22 25 28

If we could leap to the middle of the list ...

06-8: The Best of Both Worlds

4 8 12 15 22 25 28

06-9: The Best of Both Worlds

4 8 12 15 22 25 28

Move the initial pointer to the middle of the list:

4 8 12 15 22 25 28

We’ve cut our search time in half! Have we changed

the Θ() running time?

06-10: The Best of Both Worlds

4 8 12 15 22 25 28

Move the initial pointer to the middle of the list:

4 8 12 15 22 25 28

We’ve cut our search time in half! Have we changed

the Θ() running time?

Repeat the process!

06-11: The Best of Both Worlds

4 8 12 15 22 25 28

4 8 12 15 22 25 28

4 8 12 15 22 25 28

06-12: The Best of Both Worlds

Grab the first element of the list:

4 8 12 15 22 25 28

Give it a good shake -

4

8

12

15

22

25

28

06-13: Binary Trees

Binary Trees are Recursive Data Structures

Base Case: Empty Tree

Recursive Case: Node, consiting of:

Left Child (Tree)

Right Child (Tree)

Data

06-14: Binary Tree Examples

The following are all Binary Trees (Though not BinarySearch Trees)

A

B C

D E

F

A

B

C

D

A

B C

F GD E

06-15: Tree Terminology

Parent / Child

Leaf node

Root node

Edge (between nodes)

Path

Ancestor / Descendant

Depth of a node n

Length of path from root to n

Height of a tree

(Depth of deepest node) + 1

06-16: Full Binary Tree

Each node has 0 or 2 children

Full Binary Trees

Not Full Binary Trees

06-17: Complete Binary Tree

Can be built by starting at the root, and filling thetree by levels from left to right

Complete Binary Trees

Not Complete Binary Trees

06-18: Binary Search Trees

Binary Trees

For each node n, (value stored at node n) ≥ (valuestored in left subtree)

For each node n, (value stored at node n) < (valuestored in right subtree)

06-19: Example Binary Search Trees

D

C

B

A

D

B F

E GA c

A

B

C

D

06-20: Implementing BSTs

Each Node in a BST is implemented as a class:

public class Node {public Comparable data;public Node left;public Node right;

}

06-21: Implementing BSTs

public class Node {

public Node(Comparable data, Node left, Node right) {

this.data = data;

this.left = left;

this.right = right;

}

public Node left() {

return left;

}

public Node setLeft(Node newLeft) {

left = newLeft

}

... (etc)

private Comparable data;

private Node left;

private Node right;

}

06-22: Finding an Element in a BST

Binary Search Trees are recursive data structures,so most operations on them will be recursive aswell

Recall how to write a recursive algorithm ...

06-23: Writing a Recursive Algorithm

Determine a small version of the problem, whichcan be solved immediately. This is the base case

Determine how to make the problem smaller

Once the problem has been made smaller, we canassume that the function that we are writing willwork correctly on the smaller problem (RecursiveLeap of Faith)

Determine how to use the solution to thesmaller problem to solve the larger problem

06-24: Finding an Element in a BST

First, the Base Case – when is it easy to determineif an element is stored in a Binary Search Tree?

06-25: Finding an Element in a BST

First, the Base Case – when is it easy to determineif an element is stored in a Binary Search Tree?

If the tree is empty, then the element can’t bethere

If the element is stored at the root, then theelement is there

06-26: Finding an Element in a BST

Next, the Recursive Case – how do we make theproblem smaller?

06-27: Finding an Element in a BST

Next, the Recursive Case – how do we make theproblem smaller?

Both the left and right subtrees are smallerversions of the problem. Which one do we use?

06-28: Finding an Element in a BST

Next, the Recursive Case – how do we make theproblem smaller?

Both the left and right subtrees are smallerversions of the problem. Which one do we use?

If the element we are trying to find is < theelement stored at the root, use the left subtree.Otherwise, use the right subtree.

06-29: Finding an Element in a BST

Next, the Recursive Case – how do we make theproblem smaller?

Both the left and right subtrees are smallerversions of the problem. Which one do we use?

If the element we are trying to find is < theelement stored at the root, use the left subtree.Otherwise, use the right subtree.

How do we use the solution to the subproblem tosolve the original problem?

06-30: Finding an Element in a BST

Next, the Recursive Case – how do we make theproblem smaller?

Both the left and right subtrees are smallerversions of the problem. Which one do we use?

If the element we are trying to find is < theelement stored at the root, use the left subtree.Otherwise, use the right subtree.

How do we use the solution to the subproblem tosolve the original problem?

The solution to the subproblem is the solutionto the original problem (this is not always thecase in recursive algorithms)

06-31: Finding an Element in a BST

To find an element e in a Binary Search Tree T :

If T is empty, then e is not in T

If the root of T contains e, then e is in T

If e < the element stored in the root of T :

Look for e in the left subtree of T

Otherwise

Look for e in the right subtree of T

06-32: Finding an Element in a BST

boolean find(Node tree, Comparable elem) {if (tree == null)

return false;if (elem.compareTo(tree.element()) == 0)

return true;if (elem.compareTo(tree) < 0)

return find(tree.left(), elem);else

return find(tree.right(), elem);}

06-33: Printing out a BST

To print out all element in a BST:

Print all elements in the left subtree, in order

Print out the element at the root of the tree

Print all elements in the right subtree, in order

06-34: Printing out a BST

To print out all element in a BST:

Print all elements in the left subtree, in order

Print out the element at the root of the tree

Print all elements in the right subtree, in order

Each subproblem is a smaller version of theoriginal problem – we can assume that arecursive call will work!

06-35: Printing out a BST

What is the base case for printing out a BinarySearch Tree – what is an easy tree to print out?

06-36: Printing out a BST

What is the base case for printing out a BinarySearch Tree – what is an easy tree to print out?

An empty tree is extremely easy to print out – donothing!

Code for printing a BST ...

06-37: Printing out a BST

void print(Node tree) {if (tree != null) {

print(tree.left());System.out.println(tree.element());print(tree.right());

}}

06-38: Printing out a BST

Examples

06-39: Tree Traversals

PREORDER Traversal

Do operation on root of the tree

Traverse left subtree

Traverse right subtree

INORDER Traversal

Traverse left subtree

Do operation on root of the tree

Traverse right subtree

POSTORDER Traversal

Traverse left subtree

Traverse right subtree

Do operation on root of the tree

06-40: PREORDER Examples

06-41: POSTORDER Examples

06-42: INORDER Examples

06-43: BST Minimal Element

To find the minimal element in a BST:

Base Case: When is it easy to find the smallestelement in a BST?

Recursive Case: How can we make the problemsmaller?

How can we use the solution to the smallerproblem to solve the original problem?

06-44: BST Minimal Element

To find the minimal element in a BST:Base Case:

When is it easy to find the smallest element in aBST?

06-45: BST Minimal Element

To find the minimal element in a BST:Base Case:

When is it easy to find the smallest element in aBST?

When the left subtree is empty, then theelement stored at the root is the smallestelement in the tree.

06-46: BST Minimal Element

To find the minimal element in a BST:Recursive Case:

How can we make the problem smaller?

06-47: BST Minimal Element

To find the minimal element in a BST:Recursive Case:

How can we make the problem smaller?

Both the left and right subtrees are smallerversions of the same problem

How can we use the solution to a smaller problemto solve the original problem?

06-48: BST Minimal Element

To find the minimal element in a BST:Recursive Case:

How can we make the problem smaller?

Both the left and right subtrees are smallerversions of the same problem

How can we use the solution to a smaller problemto solve the original problem?

The smallest element in the left subtree is thesmallest element in the tree

06-49: BST Minimal Element

Comparable minimum(Node tree) {if (tree == null)

return null;if (tree.left() == null)

return tree.element();else

return minimum(tree.left());}

06-50: BST Minimal Element

Iterative Version

Comparable minimum(Node tree) {if (tree == null)

return null;while (tree.left() != null)

tree = tree.left();return tree.element();

}

06-51: Inserting e into BST T

What is the base case – an easy tree to insert anelement into?

06-52: Inserting e into BST T

What is the base case – an easy tree to insert anelement into?

An empty tree

Create a new tree, containing the element e

06-53: Inserting e into BST T

Recursive Case: How do we make the problemsmaller?

06-54: Inserting e into BST T

Recursive Case: How do we make the problemsmaller?

The left and right subtrees are smaller versionsof the same problem.

How do we use these smaller versions of theproblem?

06-55: Inserting e into BST T

Recursive Case: How do we make the problemsmaller?

The left and right subtrees are smaller versionsof the same problem

Insert the element into the left subtree if e ≤value stored at the root, and insert the elementinto the right subtree if e > value stored at theroot

06-56: Inserting e into BST T

Base case – T is empty:

Create a new tree, containing the element e

Recursive Case:

If e is less than the element at the root of T ,insert e into left subtree

If e is greater than the element at the root of T ,insert e into the right subtree

06-57: Tree Manipulation in Java

Tree manipulation functions return trees

Insert method takes as input the old tree and theelement to insert, and returns the new tree, withthe element inserted

Old value (pre-insertion) of tree will bedestroyed

To insert an element e into a tree T:

T = insert(T, e);

06-58: Inserting e into BST T

Node insert(Node tree, Comparable elem) {if (tree == null) {

return new Node(elem);if (elem.compareTo(tree.element() <= 0)) {

tree.setLeft(insert(tree.left(), elem));return tree;

} else {tree.setRight(insert(tree.right(), elem));return tree;

}}

06-59: Deleting From a BST

Removing a leaf:

06-60: Deleting From a BST

Removing a leaf:

Remove element immediately

06-61: Deleting From a BST

Removing a leaf:

Remove element immediately

Removing a node with one child:

06-62: Deleting From a BST

Removing a leaf:

Remove element immediately

Removing a node with one child:

Just like removing from a linked list

Make parent point to child

06-63: Deleting From a BST

Removing a leaf:

Remove element immediately

Removing a node with one child:

Just like removing from a linked list

Make parent point to child

Removing a node with two children:

06-64: Deleting From a BST

Removing a leaf:

Remove element immediately

Removing a node with one child:

Just like removing from a linked list

Make parent point to child

Removing a node with two children:

Replace node with largest element in leftsubtree, or the smallest element in the rightsubtree

06-65: Comparable vs. .key() method

We have been storing “Comparable” elements inBSTs

Alternately, could use a “key()” method – elementsstored in BSTs must implement a key() method,which returns an integer.

We can combine the two methods

Each element stored in the tree has a key()method

key() method returns Comparable class

06-66: BST Implementation Details

Use BSTs to implement Ordered List ADT

Operations

Insert

Find

Remove

Print in Order

The specification (interface) should not specify animplementation

Allow several different implementations of thesame interface

06-67: BST Implementation Details

BST functions require the root of the tree be sent inas a parameter

Ordered list functions should not containimplementation details!

What should we do?

06-68: BST Implementation Details

BST functions require the root of the tree be sent inas a parameter

Ordered list functions should not containimplementation details!

What should we do?

Private variable, holds root of the tree

Private recursive methods, require root as anargument

Public methods call private methods, passing inprivate root